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Featured Items

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The Role of Innovation in Achieving Structural Transformation and Sustainable Development: The Case of African Economies

Adesida, Olugbenga; wa Githinji, Mwangi

2026-03-03

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Innovating Healthcare Leadership: Harnessing the Power of Effective Teams for Organizational Excellence

Kurup, Viji; Hersey, Denise; Ahuja, Nita

Leading in teams has become the norm in modern academia. For health science educators who are in leadership roles or aspiring for one to be, it is imperative to be familiar with the latest evidence on how to lead teams and how to function within a team. There are distinguishing characteristics of effective teams compared to those that are ineffective and inefficient. It is important to have a shared vision, explicit goals and outcomes, and defined channels for communication and collaboration. When deciding the composition of teams, ensuring diversity of demographics, thought, experiences, and expertise leads to success. One framework for high performing teams is Hawkins’ 5C’s model based on Clarifying, Commissioning, Co-creating, Connecting, and Core Learning to explain the key activities a team can employ to consistently raise their performance. Once a team is created, a project management tool such as RASCI (Responsible, Accountable, Supportive, Consulted, and Informed) can be used to assign responsibilities to its members. This tool outlines who is responsible, accountable, supportive, and consults or receives information. Effective team leaders are great mentors, seek opportunities for professional development, promote opportunities for others, prioritize communication, and maintain a high standard of ethics. It is natural for teams to have conflicts. It is important to identify productive conflicts that lead to growth and innovation, and to quickly resolve conflicts that will undermine the team’s work. Ineffective teams can lead to poor employee morale, apathy, and sometimes even counter-productive behavior. These behaviors need to be identified and addressed early. The fatal mistakes seen in ineffective teams are absence of trust, fear of conflict, lack of commitment, avoidance of accountability, and inattention to results. Leading a team in the health sciences can be both exciting and rewarding, and result in personal growth as well as career advancement for team members, while contributing positively to the institutional mission.

2026

Recent Submissions

  • PublicationOpen Access
    Innovating Healthcare Leadership: Harnessing the Power of Effective Teams for Organizational Excellence
    (2026) Kurup, Viji; Hersey, Denise; Ahuja, Nita
    Leading in teams has become the norm in modern academia. For health science educators who are in leadership roles or aspiring for one to be, it is imperative to be familiar with the latest evidence on how to lead teams and how to function within a team. There are distinguishing characteristics of effective teams compared to those that are ineffective and inefficient. It is important to have a shared vision, explicit goals and outcomes, and defined channels for communication and collaboration. When deciding the composition of teams, ensuring diversity of demographics, thought, experiences, and expertise leads to success. One framework for high performing teams is Hawkins’ 5C’s model based on Clarifying, Commissioning, Co-creating, Connecting, and Core Learning to explain the key activities a team can employ to consistently raise their performance. Once a team is created, a project management tool such as RASCI (Responsible, Accountable, Supportive, Consulted, and Informed) can be used to assign responsibilities to its members. This tool outlines who is responsible, accountable, supportive, and consults or receives information. Effective team leaders are great mentors, seek opportunities for professional development, promote opportunities for others, prioritize communication, and maintain a high standard of ethics. It is natural for teams to have conflicts. It is important to identify productive conflicts that lead to growth and innovation, and to quickly resolve conflicts that will undermine the team’s work. Ineffective teams can lead to poor employee morale, apathy, and sometimes even counter-productive behavior. These behaviors need to be identified and addressed early. The fatal mistakes seen in ineffective teams are absence of trust, fear of conflict, lack of commitment, avoidance of accountability, and inattention to results. Leading a team in the health sciences can be both exciting and rewarding, and result in personal growth as well as career advancement for team members, while contributing positively to the institutional mission.
  • PublicationOpen Access
  • PublicationOpen Access
    Use of physical intuition and imagistic simulation in expert problem solving
    (1994) Clement, John
    It is quite natural to suppose that the. knowledge used by experts in science is abstract and that the knowledge used by novices is concrete. This chapter discusses evidence from thinking-aloud case studies that indicates that part of the knowledge used by expert problem solvers consists of concrete physical intuitions rather than abstract verbal principles or equations.
  • PublicationOpen Access
    Physics Constrained Neural Collision Operators for Variable Hard Sphere Surrogates and Ab Initio Angle Prediction in Direct Simulation Monte Carlo
    (2026-02-15) Roohi, Ehsan; Shoja-sani, Ahmad; Stefanov, Stefan
    The Direct Simulation Monte Carlo (DSMC) method is the gold standard for non-equilibrium rarefied gas dynamics, yet its computational cost can be prohibitive, especially for near-continuum regimes and high-fidelity ab initio potentials. This work develops a unified, physics-constrained neural-operator framework that accelerates DSMC while preserving physical invariants and stochasticity required for long-time kinetic simulations. First, we introduce a local neural collision kernel replacing the phenomenological Variable Hard Sphere (VHS) model. To overcome the variance suppression and artificial cooling inherent to purely deterministic regression surrogates, we augment inference with a physics-constrained stochastic layer. Controlled latent-noise injection restores thermal fluctuations, while cellwise moment-matching strictly enforces momentum and kinetic-energy conservation. Remarkably, this operator exhibits zero-shot spatial and thermodynamic generalization: a model trained exclusively on 1D Couette flow accurately simulates a complex 2D lid-driven cavity, capturing high-order non-equilibrium moments without retraining. Second, to bypass the extreme cost of quantum-mechanical scattering, we develop a dedicated ab initio neural operator for the J¨ager interaction potential. Trained via a physics harvesting strategy on large-scale collision pairs, it efficiently captures the high-energy scattering dynamics dominating hypersonic regimes. Validated on a Mach 10 rarefied argon flow over a cylinder, the framework reproduces transport behaviors and shock features with high fidelity, achieving an approximate 20% cost reduction relative to direct numerical integration. Collectively, this work establishes physics-constrained neural operators as accurate, stable, and efficient drop-in surrogates for DSMC collision dynamics across both engineering VHS setups and ab initio hypersonic simulations.
  • PublicationOpen Access
    Resolving Cryogenic and Hypersonic Rarefied Flows via Deep Learning-Accelerated Lennard-Jones DSMC
    (2026-02-15) Shoja-sani, Ahmad; Roohi, Ehsan; Stefanov, Stefan
    Integrating the physically realistic Lennard-Jones (LJ) potential into the Direct Simulation Monte Carlo (DSMC) framework has historically been hindered by the computational cost of evaluating complex scattering dynamics. This study presents a high-fidelity, machine-learning-accelerated framework that bridges the gap between rigorous molecular physics and large-scale kinetic simulations. This new approach is implemented in the standard Bird's suite of DSMC algorithms (DSMC1, DSMC1S, and DS2V), offering a high-precision platform for rarefied-gas studies. To achieve this, two problems are addressed: incorporating Lennard-Jones-specific properties into the inherently total cross-section concept of the method and replacing the computationally intensive particle-scattering process with a surrogate machine-learning model. As a result, a universal Variable Effective Diameter (VED) model is developed through local viscosity matching, ensuring accurate capture of attractive-repulsive interactions across a wide range of temperatures, a critical advance over traditional models limited to narrow thermal bands. The surrogate model, crucial for the framework's efficiency, employs a Deep Operator Network (DeepONet) as a high-performance substitute for the computationally intensive LJ scattering integral. The framework reveals critical physical insights often missed by standard models. The framework is validated against three canonical problems: shock waves in helium and argon, supersonic Couette flow at low temperature, and hypersonic cylinder flow at two Mach numbers. In the argon shock-wave problem, we showed that although the density profile of the Variable Hard Sphere (VHS) model does not match the experimental data, its velocity distribution function follows the LJ prediction. In supersonic Couette flow with cryogenic walls (40 K), the LJ model predicts a smaller shear stress than the VHS model, highlighting the dominant role of long-range attractive forces in low-temperature shear layers. In parallel, for hypersonic flow over a cylinder at Mach=10, the LJ and VHS results agree well, even in the wake region where temperatures range above 800 K; in this regime, high-energy repulsive collisions dominate, rendering the attractive potential well negligible. As we reduced the cylinder and incoming flow temperatures to the cryogenic regime (Mach 5, Tw=40K), profound deviations became apparent: the LJ model predicted a larger, more elongated wake vortex than the VHS model, a direct macroscopic manifestation of long-range attractive forces that reduce the local effective viscosity. By leveraging Scientific Machine Learning (SciML)-based operator learning, the DeepONet surrogate preserves the intricate balance of molecular forces while accelerating the collision subroutine by 40% and reducing total simulation wall-clock time by 36%. This work establishes a scalable, physically grounded, and computationally efficient pathway for high-fidelity kinetic modeling in the era of scientific machine learning, while providing fluid physical insights beyond standard VHS predictions.
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